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👋 Hi~

  • 🔭 My name is Hanlin Zhou (周涵林).
  • 🌱 I’m a master's student at Zhejiang University of Technology (ZJUT).
  • 👯 I’m interested in Federated Learning (FL) and Graph Neural Networks (GNNs).
  • Please feel free to contact me.
  • WeChat: poipoipoi8886
  • Email: [email protected]
  • 知乎: URL

Hanlin Zhou's Projects

awesome-quantum-machine-learning icon awesome-quantum-machine-learning

Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web

cpf icon cpf

The official code of WWW2021 paper: Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework

dense icon dense

Official PyTorch implementation of DENSE (NeurIPS 2022)

drl-urban-planning icon drl-urban-planning

A deep reinforcement learning (DRL) based approach for spatial layout of land use and roads in urban communities.

easyedit icon easyedit

An Easy-to-use Knowledge Editing Framework for LLMs.

fccl icon fccl

CVPR2022 - Learn From Others and Be Yourself in Heterogeneous Federated Learning

fedapen icon fedapen

This repository contains the official implementation of the paper entitled with "FedAPEN: Personalized Cross-silo Federated Learning with Adaptability to Statistical Heterogeneity".

fedgcn icon fedgcn

Official Code for FedGCN [NeurIPS 2023]

fedgh icon fedgh

FedGH: Heterogeneous Federated Learning with Generalized Global Header (MM'23)

fedmd_clean icon fedmd_clean

FedMD: Heterogenous Federated Learning via Model Distillation

fednh icon fednh

Code release for Tackling Data Heterogeneity in Federated Learning with Class Prototypes

fedpcl icon fedpcl

[NeurIPS'22 Spotlight] Federated Learning from Pre-Trained Models: A Contrastive Learning Approach

fedppn icon fedppn

The official code of the paper: Model-Heterogeneous Federated Graph Learning with Prototype Propagation Network

fedproto icon fedproto

[AAAI'22] FedProto: Federated Prototype Learning across Heterogeneous Clients

fedrolex icon fedrolex

[NeurIPS 2022] "FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction" by Samiul Alam, Luyang Liu, Ming Yan, and Mi Zhang

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